Can machine-learning models overcome biased datasets?

For instance, if a dataset contains mostly images of white men, then a facial-recognition model trained with this data may be less accurate for women or people with different skin tones. A group of researchers at MIT, in collaboration with researchers at Harvard University and Fujitsu, Ltd., sought to understand when and how a machine-learning model is capable of overcoming this kind of dataset bias. They used an approach from neuroscience to study how training data affects whether an artificial neural network can learn to recognize objects it has not seen before. A neural network is a machine-learning model that mimics the human brain in the way it contains layers of interconnected nodes, or "neurons," that process data. The new results show that diversity in training data has a major influence on whether a neural network is able to overcome bias, but at the same time dataset diversity can degrade the network's performance.